Enhancing the navigability in a social network of smart objects: a Shapley-value based approach

The Internet of Things (IoT) holds the promise to interconnect any possible object capable of providing useful information about the physical world for the benefit of humans' quality of life. The increasing number of heterogeneous objects that the IoT has to manage introduces crucial scalability issues that still need appropriate solutions. In this respect, one promising proposal is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with each other (adhering to rules set by their owners). "Friend" objects exchange data in a distributed manner and this avoids centralized solutions to implement major functions, such as: node discovery, information search, and trustworthiness management. However, the number and types of established friendships affect network navigability. This issue is the focus of this paper, which proposes an efficient, distributed and dynamic solution for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection mechanism relies on a game theoretic model and a Shapley-value based algorithm. Two different utility functions are defined and evaluated based on either a group degree centrality and an average local clustering parameter. The comparison in terms of global navigability is measured in terms of average path length for the interconnection of any couple of nodes in the network. Results show that the group degree centrality brings to an enhanced degree of navigability thanks to the ability to create a suitable core of hubs

Using a distributed Shapley-value based approach to ensure navigability in a social network of smart objects

The huge number of nodes that is expected to join the Internet of Things in the short term will add major scalability issues to several procedures. A recent promising approach to these issues is based on social networking solutions to allow objects to autonomously establish social relationships. Every object in the resulting Social IoT (SIoT) exchanges data with its friend objects in a distributed manner to avoid the need for centralized solutions to implement major functionalities, such as: node discovery, information search and trustworthiness management. However, the number and types of established friendship affects network navigability. This paper addresses this issue proposing an efficient, distributed and dynamic strategy for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection model relies on a Shapley-value based algorithm mapping the friendship selection process in the SIoT onto the coalition formation problem in a corresponding cooperative game. The obtained results show that the proposed solution is able to ensure global navigability, measured in terms of average path length among two nodes in the network, by means of a distributed and wise selection of the number of friend objects a node has to handle

Collective navigation of complex networks: Participatory greedy routing

Many networks are used to transfer information or goods, in other words, they are navigated. The larger the network, the more difficult it is to navigate efficiently. Indeed, information routing in the Internet faces serious scalability problems due to its rapid growth, recently accelerated by the rise of the Internet of Things. Large networks like the Internet can be navigated efficiently if nodes, or agents, actively forward information based on hidden maps underlying these systems. However, in reality most agents will deny to forward messages, which has a cost, and navigation is impossible. Can we design appropriate incentives that lead to participation and global navigability? Here, we present an evolutionary game where agents share the value generated by successful delivery of information or goods. We show that global navigability can emerge, but its complete breakdown is possible as well. Furthermore, we show that the system tends to self-organize into local clusters of agents who participate in the navigation. This organizational principle can be exploited to favor the emergence of global navigability in the system.Comment: Supplementary Information and Videos: https://koljakleineberg.wordpress.com/2016/11/14/collective-navigation-of-complex-networks-participatory-greedy-routing

Social Structure Analysis in Internet of Vehicles

International audienceInternet, in its most recent evolution, is going to be the playground where a multitude of heterogeneous interconnected " things " autonomously exchange information to accomplish some tasks or to provide a service. Recently, the idea of giving to those smart devices the capability to organize themselves according to a social structure, gave birth to the so-called paradigm of the Social Internet of Things. The expected benefits of SIoT range from the enhanced effectiveness, scalability and speed of the navigability of the network of interconnected objects, to the provision of a level of trustworthiness that can be established by averaging the social relationships among things that are " friends ". Bearing in mind the beneficial effects of social components in IoT, we consider a social structure in a vehicular context i.e., Social Internet of Vehicles (SIoV). In SIoV, smart vehicles build social relationships with other social objects they might come into contact, with the intent of creating an overlay social network to be exploited for information search and dissemination for vehicular applications. In this paper, we aim to investigate the social behavior of vehicles in SIoV and how it is affected by mobility patterns. Specifically, through the analysis of simulated traffic traces, we distinguish friendly and acquaintance vehicles based on the encounter time and connection maintenanc

Complex networks as an emerging property of hierarchical preferential attachment

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.Comment: 12 pages, 7 figure

Adaptive Dynamics of Realistic Small-World Networks

Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven and non-homogeneously distributed. We investigate the same algorithm in the case where some vertices are more popular destinations for searches than others, for example obeying power-laws. We find that the algorithm adapts and adjusts the networks according to the distributions, leading to improved performance. The ability of the dynamic process to adapt and create small worlds in such diverse settings suggests a possible mechanism by which such networks appear in nature